{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Describe the purpose of the data set you selected \n",
    "- *Why was this data collected in the first place?* - The data set selected is the \"UNSW_NB15\" network traffic data set. The data set was created to evalutate Network Intrusion Detection Systems (NIDS). According to the creators of the data set, N. Moustafa and J. Slay from the Australian Defence Force Academy, the \"quality of a NIDS data set reflects two important characteristics: comprehensive reflection of contemporary threats and an inclusive normal range of traffic [1].\" Before this data set was generated, they argued that existing data sets used to train and test NIDS were not representative of current traffic flows and threats.\n",
    "- Describe how you would define and measure the outcomes from the dataset. That is: \n",
    "    - *Why is this data important?* - The importance of this data set is that it will allow NIDS to be evaluted better, which will increase their performance, increase their protective power and reduce the chance of false positives and false negatives.\n",
    "    - *How do you know if you have mined useful knowledge from the dataset?* - We will know we mined useful knowledge from the data set if we can determine, based on a collection of packets of traffic data, whether the features of those packets indicate an attack or just normal traffic.\n",
    "    - *How would you measure the effectiveness of a good prediction algorithm? Be specific.* Finding a model that predicts from the network traffic whether an attack is occuring or not, and, if there is an attack, which category of attack if occuring, will measure the effectiveness of the model. Determining if an attack is occuring is the basis of the model and with refinement, we will try to search for categorizing the attack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import sys\n",
    "\n",
    "warnings.simplefilter('ignore', DeprecationWarning)\n",
    "pd.set_option('display.max_columns', None)    # set the max columns to show to unlimited\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 2.7.12 |Anaconda 2.3.0 (64-bit)| (default, Jun 29 2016, 11:07:13) [MSC v.1500 64 bit (AMD64)]\n",
      "Pandas version 0.17.1\n"
     ]
    }
   ],
   "source": [
    "print('Python version ' + sys.version)\n",
    "print('Pandas version ' + pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load UNSW_NB15 into a Pandas dataframe\n",
    "# ref: http://stackoverflow.com/questions/18664712/split-function-add-xef-xbb-xbf-n-to-my-list (2 get rid of UTF-8 BOM)\n",
    "df = pd.read_csv('UNSW_NB15_training_set.csv', encoding='utf-8-sig')  # specifing encoding to get rid of the UTF- Byte order Mark (BOM) in the id field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe the meaning and type of data (scale, values, etc.) for each attribute in the data file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UNSW_NB15_training_set\n",
    "- A Network Intrusion Data Set.\n",
    "    - 82332 rows x 45 columns\n",
    "        - **id** *int* The id of the record\n",
    "        - **dur** *float* Record's total duration\n",
    "        - **proto** *Nominal* Tranaction Protocol\n",
    "        - **service** *Nominal* http, ftp, ssh, dns, ..., else(-)\n",
    "        - **state** *Nominal* The state and its dependent protocol e.g. ACC, CLO, FIN, INT, ..., else (-)\n",
    "\t\t- **spkts** *int* Source to destination packet count\n",
    "\t\t- **dpkts** *int* Destination to source packet count \n",
    "\t\t- **sbytes** *int* Source to destination bytes\n",
    "\t\t- **dbytes** *int* Destination to source bytes\n",
    "\t\t- **rate** *float* \n",
    "\t\t- **sttl** *int* Source to destination bytes\n",
    "\t\t- **dttl** *int* Destination to source time to live\n",
    "\t\t- **sload** *float* Source bits per second\n",
    "\t\t- **dload** *float* Destination bits per second\n",
    "\t\t- **sloss** *int* Source packets retransmitted or dropped\n",
    "\t\t- **dloss** *int* Destination packets retransmitted or dropped\n",
    "\t\t- **sinpkt** *float* Source inter-packet arrival time(mSec)\n",
    "\t\t- **dinpkt** *float* Destination inetr-packet arrival time(mSec)\n",
    "\t\t- **sjit** *float* Source jitter (mSec)\n",
    "\t\t- **djit** *float* Destination jitter(mSec)\n",
    "\t\t- **swin** *int* Source TCP window advertisment\n",
    "\t\t- **stcpb** *int* Source TCP sequence number\n",
    "\t\t- **dtcpb** *int* Destination TCP sequence number\n",
    "\t\t- **dwin** *int* Destination TCP window advertisment\n",
    "\t\t- **tcprtt** *float* The sum of 'synack' and 'ackdat' of the TCP\n",
    "\t\t- **synack** *float* The time between the SYN and the SYN_ACK packets of the TCP\n",
    "\t\t- **ackdat** *float* The time between the SYN_ACK and the ACK packets of the TCP\n",
    "\t\t- **smean** *int* Mean of the flow packet size transmitted by the sre\n",
    "\t\t- **dmean** *int* Mean of the flow packet size transmitted by the dst\n",
    "\t\t- **trans_depth** *int* The depth into the connection of the http request/response transaction\n",
    "\t\t- **response_body_len** *int* The content size of the data transferred from the server's http service\n",
    "\t\t- **ct_srv_src** *int* No. of Connection that contain the same service (14) and source address in 100 connections according to the last time\n",
    "\t\t- **ct_state_ttl** *int* No. for each state (6) according to specific range if value for source/destination time to live\n",
    "\t\t- **ct_dst_ltm** *int* No of connectiuon of the same destination address (3) in 100 connections according to the last time\n",
    "\t\t- **ct_src_dport_ltm** *int* No. of connection of the same source address and the destination port in 100 connections according to the last time\n",
    "\t\t- **ct_dst_sport_ltm** *int* No. of connnection of the same destination address(3) and the source port (2) in 100 connection according to the last time (26).\n",
    "\t\t- **ct_dst_src_ltm** *int* No connection of the same cource (1) and the destination (3) address in 100 connection according to the last time (26)\n",
    "\t\t- **is_ftp_login** *int* (binary) If the ftp session is accessed by user and password then 1 else 0\n",
    "\t\t- **ct_ftp_cmd** *int* No. of flows that has a command in ftp session\n",
    "\t\t- **ct_flw_http_mthd** *int* No. of flows that has methof such as Get and Post in http service\n",
    "\t\t- **ct_src_ltm** *int* No of connections of the same source address (1) in 100 connection according to the last time(26).\n",
    "\t\t- **ct_srv_dst** *int* No. of connectio that conbtain the same service and destination address in 100 connection according to the last time\n",
    "\t\t- **is_sm_ips_ports** *int* (binary) If source equals to destination (3) IP addresses and port numbers (2)(4) are equal, this variable takes value 1 else 0\n",
    "\t\t- **attack_cat** *Nominal* The name of each attack category. In this data set, nine categories (e.g. Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms) (Labeled Feature)\n",
    "\t\t- **label** *int* (Binary) 0 for normal and 1 for attack records (Labeled feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 82332 entries, 0 to 82331\n",
      "Data columns (total 45 columns):\n",
      "id                   82332 non-null int64\n",
      "dur                  82332 non-null float64\n",
      "proto                82332 non-null object\n",
      "service              82332 non-null object\n",
      "state                82332 non-null object\n",
      "spkts                82332 non-null int64\n",
      "dpkts                82332 non-null int64\n",
      "sbytes               82332 non-null int64\n",
      "dbytes               82332 non-null int64\n",
      "rate                 82332 non-null float64\n",
      "sttl                 82332 non-null int64\n",
      "dttl                 82332 non-null int64\n",
      "sload                82332 non-null float64\n",
      "dload                82332 non-null float64\n",
      "sloss                82332 non-null int64\n",
      "dloss                82332 non-null int64\n",
      "sinpkt               82332 non-null float64\n",
      "dinpkt               82332 non-null float64\n",
      "sjit                 82332 non-null float64\n",
      "djit                 82332 non-null float64\n",
      "swin                 82332 non-null int64\n",
      "stcpb                82332 non-null int64\n",
      "dtcpb                82332 non-null int64\n",
      "dwin                 82332 non-null int64\n",
      "tcprtt               82332 non-null float64\n",
      "synack               82332 non-null float64\n",
      "ackdat               82332 non-null float64\n",
      "smean                82332 non-null int64\n",
      "dmean                82332 non-null int64\n",
      "trans_depth          82332 non-null int64\n",
      "response_body_len    82332 non-null int64\n",
      "ct_srv_src           82332 non-null int64\n",
      "ct_state_ttl         82332 non-null int64\n",
      "ct_dst_ltm           82332 non-null int64\n",
      "ct_src_dport_ltm     82332 non-null int64\n",
      "ct_dst_sport_ltm     82332 non-null int64\n",
      "ct_dst_src_ltm       82332 non-null int64\n",
      "is_ftp_login         82332 non-null int64\n",
      "ct_ftp_cmd           82332 non-null int64\n",
      "ct_flw_http_mthd     82332 non-null int64\n",
      "ct_src_ltm           82332 non-null int64\n",
      "ct_srv_dst           82332 non-null int64\n",
      "is_sm_ips_ports      82332 non-null int64\n",
      "attack_cat           82332 non-null object\n",
      "label                82332 non-null int64\n",
      "dtypes: float64(11), int64(30), object(4)\n",
      "memory usage: 28.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify data quality: Explain any missing values, duplicate data, and outliers.\n",
    "    - Are those mistakes? How do you deal with these problems? Be specific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dur</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>dload</th>\n",
       "      <th>sloss</th>\n",
       "      <th>dloss</th>\n",
       "      <th>sinpkt</th>\n",
       "      <th>dinpkt</th>\n",
       "      <th>sjit</th>\n",
       "      <th>djit</th>\n",
       "      <th>swin</th>\n",
       "      <th>stcpb</th>\n",
       "      <th>dtcpb</th>\n",
       "      <th>dwin</th>\n",
       "      <th>tcprtt</th>\n",
       "      <th>synack</th>\n",
       "      <th>ackdat</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>1.006756</td>\n",
       "      <td>18.666472</td>\n",
       "      <td>17.545936</td>\n",
       "      <td>7993.908165</td>\n",
       "      <td>13233.785563</td>\n",
       "      <td>82410.886739</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>6.454902e+07</td>\n",
       "      <td>630546.959000</td>\n",
       "      <td>4.753692</td>\n",
       "      <td>6.308556</td>\n",
       "      <td>755.394301</td>\n",
       "      <td>121.701284</td>\n",
       "      <td>6363.075100</td>\n",
       "      <td>535.180430</td>\n",
       "      <td>133.45908</td>\n",
       "      <td>1.084642e+09</td>\n",
       "      <td>1.073465e+09</td>\n",
       "      <td>128.28662</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.026669</td>\n",
       "      <td>139.528604</td>\n",
       "      <td>116.275069</td>\n",
       "      <td>0.094277</td>\n",
       "      <td>1595.371885</td>\n",
       "      <td>9.546604</td>\n",
       "      <td>1.369273</td>\n",
       "      <td>5.744923</td>\n",
       "      <td>4.928898</td>\n",
       "      <td>3.663011</td>\n",
       "      <td>7.456360</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.129743</td>\n",
       "      <td>6.468360</td>\n",
       "      <td>9.164262</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23767.345519</td>\n",
       "      <td>4.710444</td>\n",
       "      <td>133.916353</td>\n",
       "      <td>115.574086</td>\n",
       "      <td>171642.261880</td>\n",
       "      <td>151471.456091</td>\n",
       "      <td>148620.367041</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>1.798618e+08</td>\n",
       "      <td>2393000.555646</td>\n",
       "      <td>64.649620</td>\n",
       "      <td>55.708021</td>\n",
       "      <td>6182.615732</td>\n",
       "      <td>1292.378499</td>\n",
       "      <td>56724.016689</td>\n",
       "      <td>3635.305383</td>\n",
       "      <td>127.35700</td>\n",
       "      <td>1.390860e+09</td>\n",
       "      <td>1.381996e+09</td>\n",
       "      <td>127.49137</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.070854</td>\n",
       "      <td>0.055094</td>\n",
       "      <td>208.472063</td>\n",
       "      <td>244.600271</td>\n",
       "      <td>0.542922</td>\n",
       "      <td>38066.972292</td>\n",
       "      <td>11.090289</td>\n",
       "      <td>1.067188</td>\n",
       "      <td>8.418112</td>\n",
       "      <td>8.389545</td>\n",
       "      <td>5.915386</td>\n",
       "      <td>11.415191</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.638683</td>\n",
       "      <td>8.543927</td>\n",
       "      <td>11.121413</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20583.750000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.606114</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>0.014138</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>2650.176667</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>2112.951416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557928</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>17.623919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.788886e+07</td>\n",
       "      <td>2.856975e+07</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61749.250000</td>\n",
       "      <td>0.719360</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1280.000000</td>\n",
       "      <td>956.000000</td>\n",
       "      <td>111111.107200</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>15858.082275</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.409444</td>\n",
       "      <td>63.136369</td>\n",
       "      <td>3219.332412</td>\n",
       "      <td>128.459914</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.171310e+09</td>\n",
       "      <td>2.144205e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.105541</td>\n",
       "      <td>0.052595</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>59.999989</td>\n",
       "      <td>10646.000000</td>\n",
       "      <td>11018.000000</td>\n",
       "      <td>14355774.000000</td>\n",
       "      <td>14657531.000000</td>\n",
       "      <td>1000000.003000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>5.268000e+09</td>\n",
       "      <td>20821108.000000</td>\n",
       "      <td>5319.000000</td>\n",
       "      <td>5507.000000</td>\n",
       "      <td>60009.992000</td>\n",
       "      <td>57739.240000</td>\n",
       "      <td>1483830.917000</td>\n",
       "      <td>463199.240100</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>4.294950e+09</td>\n",
       "      <td>4.294881e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.821465</td>\n",
       "      <td>3.226788</td>\n",
       "      <td>2.928778</td>\n",
       "      <td>1504.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>5242880.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id           dur         spkts         dpkts  \\\n",
       "count  82332.000000  82332.000000  82332.000000  82332.000000   \n",
       "mean   41166.500000      1.006756     18.666472     17.545936   \n",
       "std    23767.345519      4.710444    133.916353    115.574086   \n",
       "min        1.000000      0.000000      1.000000      0.000000   \n",
       "25%    20583.750000      0.000008      2.000000      0.000000   \n",
       "50%    41166.500000      0.014138      6.000000      2.000000   \n",
       "75%    61749.250000      0.719360     12.000000     10.000000   \n",
       "max    82332.000000     59.999989  10646.000000  11018.000000   \n",
       "\n",
       "                sbytes           dbytes            rate          sttl  \\\n",
       "count     82332.000000     82332.000000    82332.000000  82332.000000   \n",
       "mean       7993.908165     13233.785563    82410.886739    180.967667   \n",
       "std      171642.261880    151471.456091   148620.367041    101.513358   \n",
       "min          24.000000         0.000000        0.000000      0.000000   \n",
       "25%         114.000000         0.000000       28.606114     62.000000   \n",
       "50%         534.000000       178.000000     2650.176667    254.000000   \n",
       "75%        1280.000000       956.000000   111111.107200    254.000000   \n",
       "max    14355774.000000  14657531.000000  1000000.003000    255.000000   \n",
       "\n",
       "               dttl         sload            dload         sloss  \\\n",
       "count  82332.000000  8.233200e+04     82332.000000  82332.000000   \n",
       "mean      95.713003  6.454902e+07    630546.959000      4.753692   \n",
       "std      116.667722  1.798618e+08   2393000.555646     64.649620   \n",
       "min        0.000000  0.000000e+00         0.000000      0.000000   \n",
       "25%        0.000000  1.120247e+04         0.000000      0.000000   \n",
       "50%       29.000000  5.770032e+05      2112.951416      1.000000   \n",
       "75%      252.000000  6.514286e+07     15858.082275      3.000000   \n",
       "max      253.000000  5.268000e+09  20821108.000000   5319.000000   \n",
       "\n",
       "              dloss        sinpkt        dinpkt            sjit  \\\n",
       "count  82332.000000  82332.000000  82332.000000    82332.000000   \n",
       "mean       6.308556    755.394301    121.701284     6363.075100   \n",
       "std       55.708021   6182.615732   1292.378499    56724.016689   \n",
       "min        0.000000      0.000000      0.000000        0.000000   \n",
       "25%        0.000000      0.008000      0.000000        0.000000   \n",
       "50%        0.000000      0.557928      0.010000       17.623919   \n",
       "75%        2.000000     63.409444     63.136369     3219.332412   \n",
       "max     5507.000000  60009.992000  57739.240000  1483830.917000   \n",
       "\n",
       "                djit         swin         stcpb         dtcpb         dwin  \\\n",
       "count   82332.000000  82332.00000  8.233200e+04  8.233200e+04  82332.00000   \n",
       "mean      535.180430    133.45908  1.084642e+09  1.073465e+09    128.28662   \n",
       "std      3635.305383    127.35700  1.390860e+09  1.381996e+09    127.49137   \n",
       "min         0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000   \n",
       "25%         0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000   \n",
       "50%         0.000000    255.00000  2.788886e+07  2.856975e+07    255.00000   \n",
       "75%       128.459914    255.00000  2.171310e+09  2.144205e+09    255.00000   \n",
       "max    463199.240100    255.00000  4.294950e+09  4.294881e+09    255.00000   \n",
       "\n",
       "             tcprtt        synack        ackdat         smean         dmean  \\\n",
       "count  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000   \n",
       "mean       0.055925      0.029256      0.026669    139.528604    116.275069   \n",
       "std        0.116022      0.070854      0.055094    208.472063    244.600271   \n",
       "min        0.000000      0.000000      0.000000     24.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000     57.000000      0.000000   \n",
       "50%        0.000551      0.000441      0.000080     65.000000     44.000000   \n",
       "75%        0.105541      0.052595      0.048816    100.000000     87.000000   \n",
       "max        3.821465      3.226788      2.928778   1504.000000   1500.000000   \n",
       "\n",
       "        trans_depth  response_body_len    ct_srv_src  ct_state_ttl  \\\n",
       "count  82332.000000       82332.000000  82332.000000  82332.000000   \n",
       "mean       0.094277        1595.371885      9.546604      1.369273   \n",
       "std        0.542922       38066.972292     11.090289      1.067188   \n",
       "min        0.000000           0.000000      1.000000      0.000000   \n",
       "25%        0.000000           0.000000      2.000000      1.000000   \n",
       "50%        0.000000           0.000000      5.000000      1.000000   \n",
       "75%        0.000000           0.000000     11.000000      2.000000   \n",
       "max      131.000000     5242880.000000     63.000000      6.000000   \n",
       "\n",
       "         ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  \\\n",
       "count  82332.000000      82332.000000      82332.000000    82332.000000   \n",
       "mean       5.744923          4.928898          3.663011        7.456360   \n",
       "std        8.418112          8.389545          5.915386       11.415191   \n",
       "min        1.000000          1.000000          1.000000        1.000000   \n",
       "25%        1.000000          1.000000          1.000000        1.000000   \n",
       "50%        2.000000          1.000000          1.000000        3.000000   \n",
       "75%        6.000000          4.000000          3.000000        6.000000   \n",
       "max       59.000000         59.000000         38.000000       63.000000   \n",
       "\n",
       "       is_ftp_login    ct_ftp_cmd  ct_flw_http_mthd    ct_src_ltm  \\\n",
       "count  82332.000000  82332.000000      82332.000000  82332.000000   \n",
       "mean       0.008284      0.008381          0.129743      6.468360   \n",
       "std        0.091171      0.092485          0.638683      8.543927   \n",
       "min        0.000000      0.000000          0.000000      1.000000   \n",
       "25%        0.000000      0.000000          0.000000      1.000000   \n",
       "50%        0.000000      0.000000          0.000000      3.000000   \n",
       "75%        0.000000      0.000000          0.000000      7.000000   \n",
       "max        2.000000      2.000000         16.000000     60.000000   \n",
       "\n",
       "         ct_srv_dst  is_sm_ips_ports         label  \n",
       "count  82332.000000     82332.000000  82332.000000  \n",
       "mean       9.164262         0.011126      0.550600  \n",
       "std       11.121413         0.104891      0.497436  \n",
       "min        1.000000         0.000000      0.000000  \n",
       "25%        2.000000         0.000000      0.000000  \n",
       "50%        5.000000         0.000000      1.000000  \n",
       "75%       11.000000         0.000000      1.000000  \n",
       "max       62.000000         1.000000      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.describe() calculates summary statistics the count, mean, standard deviation, min, max, and quartiles \n",
    "# of the data less the nominal (object) columns. \n",
    "df_desc_table = df.describe() # result: 41 numeric types\n",
    "df_desc_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statistics above are for numeric data types with a totoal of 41 features.  the count of 82332 indicate no missing data.  \n",
    "\n",
    "Binary Data types are **is_sm_ips_ports**, **is_ftp_login**, **label**\n",
    "- **label** Min = 0, Max = 1, which is what I would expect as it is binary in nature where 0 means it is a normal packet, while 1 means it is an attack record (or abnormal)\n",
    "- **is_sm_ips_ports**  Min = 0, Max = 1, which is what I would expect as it is binary in nature\n",
    "- ** is_ftp_login **  Min = -=0, Max = 2, I wouldn't expect max = 2 for this feature  (Marked \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>attack_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>131</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>43095</td>\n",
       "      <td>47153</td>\n",
       "      <td>39339</td>\n",
       "      <td>37000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        proto service  state attack_cat\n",
       "count   82332   82332  82332      82332\n",
       "unique    131      13      7         10\n",
       "top       tcp       -    FIN     Normal\n",
       "freq    43095   47153  39339      37000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O']) # summary statistics on the categrical data types in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dur float64\n",
      "spkts int64\n",
      "dpkts int64\n",
      "sbytes int64\n",
      "dbytes int64\n",
      "rate float64\n",
      "sttl int64\n",
      "dttl int64\n",
      "sload float64\n",
      "dload float64\n",
      "sloss int64\n",
      "dloss int64\n",
      "sinpkt float64\n",
      "dinpkt float64\n",
      "sjit float64\n",
      "djit float64\n",
      "swin int64\n",
      "stcpb int64\n",
      "dtcpb int64\n",
      "dwin int64\n",
      "tcprtt float64\n",
      "synack float64\n",
      "ackdat float64\n",
      "smean int64\n",
      "dmean int64\n",
      "trans_depth int64\n",
      "response_body_len int64\n",
      "ct_srv_src int64\n",
      "ct_state_ttl int64\n",
      "ct_dst_ltm int64\n",
      "ct_src_dport_ltm int64\n",
      "ct_dst_sport_ltm int64\n",
      "ct_dst_src_ltm int64\n",
      "is_ftp_login int64\n",
      "ct_ftp_cmd int64\n",
      "ct_flw_http_mthd int64\n",
      "ct_src_ltm int64\n",
      "ct_srv_dst int64\n",
      "is_sm_ips_ports int64\n",
      "label int64\n",
      "0\n",
      "82332\n",
      "6201\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x224be6a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CG0UZWLJkic6dO6ecnBwtWrRINptNU6dO1Zw5c+R2u9W+fXv1799fNptNqamp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\nWNz/A/7NLfnu9tFYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d637d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this could be useful http://stackoverflow.com/questions/24901766/python-how-to-get-column-names-from-pandas-dataframe-but-only-for-continuous\n",
    "df2 = df.copy(deep=True)\n",
    "df2.drop('id', axis=1, inplace=True) # 1 is the axis number ( 0 for rows 1 for columns), dropping id as it doesn't make sense to plot outliers for that column\n",
    "for col in df2.columns:\n",
    "    if(df2[col].dtype == np.float64 or df2[col].dtype == np.int64):\n",
    "        # do something\n",
    "        print df2[col].name + \" \" + str(df[col].dtype)\n",
    "\n",
    "\n",
    "# using += 1.5 IQR to determine outliers from continous/oridnal numbers. \n",
    "col_rate = df_desc_table['rate']\n",
    "df_sub2 =df[['rate']]\n",
    "#type(df_desc_table)\n",
    "#type(col_rate) # pandas.core.series.Series\n",
    "#col_rate\n",
    "#df_desc_table.keys()\n",
    "q1 = df_desc_table.iloc[4]['rate']  # 6 = 25% row of description table or Q1\n",
    "q3 = df_desc_table.iloc[6]['rate']  # 6 = 75% row of description table or Q3\n",
    "iqr = q3 - q1\n",
    "upper_outlier_threshold = q3 + (1.5 * iqr) \n",
    "lower_outlier_threshold = q1 - (1.5 * iqr)\n",
    "#df_desc_table.get_values()\n",
    "#ol_rate[np.abs(col_rate) > 2]\n",
    "# http://stackoverflow.com/questions/22697773/how-to-check-the-dtype-of-a-column-in-python-pandas\n",
    "#df_info = df.info()\n",
    "#type(df_info) # NoneType\n",
    "# type(df.columns) # pandas.core.index.Index\n",
    "col_index = df.columns\n",
    "col_index.get_values() # Returns an array of column names \n",
    "col = df['rate']\n",
    "lower_outliers = col[col < lower_outlier_threshold]\n",
    "upper_outliers = col[col > upper_outlier_threshold]\n",
    "print len(lower_outliers)\n",
    "print len(col)\n",
    "print len(upper_outliers)\n",
    "# according to this link , outliers do not show when you use seaborn: http://stackoverflow.com/questions/28908003/matplotlib-box-plot-fliers-not-showing\n",
    "# sns.boxplot (data_to_plot, ax=ax)\n",
    "#df.boxplot(column='rate', return_type='axes')  # \n",
    "#Set up the graph parameters\n",
    "sns.set(context='notebook', style='whitegrid')\n",
    "sns.axlabel(xlabel=\"Features\", ylabel=\"Y-Axis\", fontsize=16)\n",
    "sns.boxplot(data=df[['spkts', 'dpkts']] , fliersize=3) # api: https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html\n",
    "#df_info.iloc[0][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column name:#lower_outliers:#obserations:#upper_outliers:Max Values\n",
      "dur:0:82332:5868:59.999989\n",
      "spkts:0:82332:10196:10646.0\n",
      "dpkts:0:82332:8907:11018.0\n",
      "sbytes:0:82332:9270:14355774.0\n",
      "dbytes:0:82332:12308:14657531.0\n",
      "rate:0:82332:6201:1000000.003\n",
      "sload:0:82332:6715:5268000256.0\n",
      "dload:0:82332:18112:20821108.0\n",
      "sloss:0:82332:5499:5319.0\n",
      "dloss:0:82332:11272:5507.0\n",
      "sinpkt:0:82332:5668:60009.992\n",
      "dinpkt:0:82332:4717:57739.24\n",
      "sjit:0:82332:6321:1483830.917\n",
      "djit:0:82332:8573:463199.2401\n",
      "tcprtt:0:82332:2020:3.821465\n",
      "synack:0:82332:2954:3.226788\n",
      "ackdat:0:82332:2480:2.928778\n",
      "smean:0:82332:11928:1504.0\n",
      "dmean:0:82332:9902:1500.0\n",
      "trans_depth:0:82332:7582:131.0\n",
      "response_body_len:0:82332:5657:5242880.0\n",
      "ct_srv_src:0:82332:10093:63.0\n",
      "ct_state_ttl:0:82332:1833:6.0\n",
      "ct_dst_ltm:0:82332:10479:59.0\n",
      "ct_src_dport_ltm:0:82332:11476:59.0\n",
      "ct_dst_sport_ltm:0:82332:10907:38.0\n",
      "ct_dst_src_ltm:0:82332:12789:63.0\n",
      "is_ftp_login:0:82332:678:2.0\n",
      "ct_ftp_cmd:0:82332:680:2.0\n",
      "ct_flw_http_mthd:0:82332:7580:16.0\n",
      "ct_src_ltm:0:82332:9579:60.0\n",
      "ct_srv_dst:0:82332:10040:62.0\n",
      "is_sm_ips_ports:0:82332:916:1.0\n",
      "number of features with outliers: 33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#******* \n",
    "# Code that calculates the number of outliers below and above for continous numeric features.\n",
    "# this is to help determine which particular feature to do box plots on.\n",
    "#ref: http://stackoverflow.com/questions/24901766/python-how-to-get-column-names-from-pandas-dataframe-but-only-for-continuous\n",
    "df_cont = df.select_dtypes(['float64', 'int64']) # a data frame of just continous variables\n",
    "len(df_cont.columns)\n",
    "df_cont.drop('id', axis=1, inplace=True) # 1 is the axis number ( 0 for rows 1 for columns), dropping id as it doesn't make sense to plot outliers for that column\n",
    "len(df_cont.columns)\n",
    "print \"column name:#lower_outliers:#obserations:#upper_outliers:Max Values\"\n",
    "num_features_with_outliers = 0\n",
    "for col_name in df_cont.columns:\n",
    "    q1 = df_desc_table.iloc[4][col_name]  # 4 = 25% row of description table or Q1\n",
    "    q3 = df_desc_table.iloc[6][col_name]  # 6 = 75% row of description table or Q3\n",
    "    max_val = df_desc_table.iloc[7][col_name] # 7 = max value\n",
    "    iqr = q3 - q1\n",
    "    upper_outlier_threshold = q3 + (1.5 * iqr) \n",
    "    lower_outlier_threshold = q1 - (1.5 * iqr)\n",
    "    col = df[col_name]\n",
    "    lower_outliers = col[col < lower_outlier_threshold]\n",
    "    upper_outliers = col[col > upper_outlier_threshold]\n",
    "    if (len(lower_outliers) + len(upper_outliers)) > 0:\n",
    "        num_features_with_outliers+=1\n",
    "        print col_name + \":\" + str(len(lower_outliers)) + \":\" + str(len(col)) + \":\" + str(len(upper_outliers)) + \":\" + str(max_val)\n",
    "print \"number of features with outliers: \" + str(num_features_with_outliers)\n",
    "\n",
    "#df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Based on the code above there are 33 features with outliers based on 1.5IQR standard.\n",
    "Note: Results were pulled out into excel to sort on number of outliers and max value to try and triage which variables to look\n",
    "Interesting to note: there were no lower outliers, just upper outliers.\n",
    "The top 5 features based on large number of outliers are: \n",
    "feature name (number of outliers)\n",
    "dload (18112)\n",
    "ct_dst_src_ltm (12789)\n",
    "dbytes(12308)\n",
    "smean(11928)\n",
    "ct_src_dport_ltm(11476)\n",
    "\n",
    "Sorting by max_value will help to do box plots for variables with similar magnitude:\n",
    "E.g. \n",
    "'dpkts', 'spkts' are similir in max value\n",
    "'dloss', 'sloss' are similar in max value\n",
    "'ct_dst_src_ltm', 'ct_srv_src', 'ct_src_ltm' are similar in max vax value\n",
    "\n",
    "Lets do box plots and histograms on a few ofthese to get a graphical visual and feel of our data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dcd9fd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZTZ06VU8++aRiYmI0ffp0ffnll0pKStLGjRsbukYAANCAfBoZSEpKktvt1tSpUzVs2DBJ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\nAABgcf8PZrIt+TwMi5cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dcd9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set up the graph parameters\n",
    "sns.set(context='notebook', style='whitegrid')\n",
    "sns.axlabel(xlabel=\"Features\", ylabel=\"Y-Axis\", fontsize=16)\n",
    "sns.boxplot(data=df[['dpkts', 'spkts']] , fliersize=3) # api: https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001DCD93C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000002225D940>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C0AcAwCQIfQAATILQBwDAJAh9AABMgtAHAMAkCH0AAEyC0AcAwCQIfQAATILQBwDAJAh9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d716978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['dpkts', 'spkts']].diff().hist(alpha=0.5, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the histogram for dpkts and spkts, there seems to be a lot of values very close to 0 and they seem\n",
    "to track each other in quantity. I wouldn't be surprised if they were highly correlated.They have very similar steep central distribution centered around 0. With the network setup I wouldn't expect packets to have more than 6 hops based on the network setup.  Based on the summary statistics, 75% the dpkts and spkts have greater than 12, 10 respectively with max values of 10646.000000\t11018.000000, respectively. It would be interesting to see if there may be any correlation with these values greater than 75% of the norm correlate to abnormal traffic. \n",
    "See Explore relationships for further analysis with these particular features.\n",
    "(Pearson Correlation heat map shows value of .37 , which indiates some postive correlation of small to medium strength). \n",
    "From an outlier perspective, however, it is not clear whether these packets are expected anomolies, or errors in recording.  Will leave them in the dataset for now. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent of packets that have dpkts > 10 and abnormal = 30.4111342185\n",
      "percent of packets that have dpkts > 10 and normal = 69.5888657815\n"
     ]
    }
   ],
   "source": [
    "\n",
    "denom = df[(df['dpkts'] > 10)]\n",
    "numerator = df[(df['dpkts'] > 10) & (df['label'] == 1)]\n",
    "print \"percent of packets that have dpkts > 10 and abnormal = \" + str((float(len(numerator))/len(denom)) * 100)\n",
    "numerator  = df[(df['dpkts'] > 10) & (df['label'] == 0)]\n",
    "print \"percent of packets that have dpkts > 10 and normal = \" + str((float(len(numerator))/len(denom)) * 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22424be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mg+BH2PrnPxu2AELnhRekyMj6FuZC8CNsMaofME5SkvTii6ycaUYEP8KW19uw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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x212b2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axlabel(xlabel=\"Features\", ylabel=\"Y-Axis\", fontsize=16)\n",
    "sns.boxplot(data=df[['dloss', 'sloss']] , fliersize=3) # api: https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000021A08208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000021B76048>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VlpampqamPr1hx+v1Dsimv4hYryGa8588eVJHjpyWP3BWwcApjR+TEvP9S8PrHETbUOp3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\nzeJLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a15780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['dloss', 'sloss']].diff().hist(alpha=0.5, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dloss and sloss also see to have a lot of values near 0 and also seem to have roughly the same amount. Again, would not be surprised to see these variables correlate to each other. They have very similar steep central distribution centered around 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21976dd8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//etfNX/+fDVo0EBFRUWKjo7W6dOn9fTTT8vv96uoqEgTJkzQJ598ouzsbI0Y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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b81da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axlabel(xlabel=\"Features\", ylabel=\"Y-Axis\", fontsize=16)\n",
    "sns.boxplot(data=df[['ct_dst_src_ltm', 'ct_srv_src', 'ct_src_ltm']] , fliersize=3) # api: https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000022789E10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000022E601D0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000022AA3BA8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000022B489E8>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pIs2rSUFd99fMrXqmqbpmmepkCXId5Yv5+fn58cILL1xWfq2zS4pI82pSUPfU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x225fcdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['ct_dst_src_ltm', 'ct_srv_src', 'ct_src_ltm']].diff().hist(alpha=0.5, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'ct_dst_src_ltm', 'ct_srv_src', 'ct_src_ltm' also see to have a lot of values near 0 although slighly wider spread then previous features. Again, would not be surprised to see these variables correlate to each other. They have very similar steep central distribution centered around 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:\n",
      "[    1     2     3 ..., 82330 82331 82332]\n",
      "dur:\n",
      "[  1.10000000e-05   8.00000000e-06   5.00000000e-06 ...,   8.11914000e-01\n",
      "   5.16741000e+00   1.10610100e+00]\n",
      "proto:\n",
      "[u'udp' u'arp' u'tcp' u'igmp' u'ospf' u'sctp' u'gre' u'ggp' u'ip' u'ipnip'\n",
      " u'st2' u'argus' u'chaos' u'egp' u'emcon' u'nvp' u'pup' u'xnet' u'mux'\n",
      " u'dcn' u'hmp' u'prm' u'trunk-1' u'trunk-2' u'xns-idp' u'leaf-1' u'leaf-2'\n",
      " u'irtp' u'rdp' u'netblt' u'mfe-nsp' u'merit-inp' u'3pc' u'idpr' u'ddp'\n",
      " u'idpr-cmtp' u'tp++' u'ipv6' u'sdrp' u'ipv6-frag' u'ipv6-route' u'idrp'\n",
      " u'mhrp' u'i-nlsp' u'rvd' u'mobile' u'narp' u'skip' u'tlsp' u'ipv6-no'\n",
      " u'any' u'ipv6-opts' u'cftp' u'sat-expak' u'ippc' u'kryptolan' u'sat-mon'\n",
      " u'cpnx' u'wsn' u'pvp' u'br-sat-mon' u'sun-nd' u'wb-mon' u'vmtp' u'ttp'\n",
      " u'vines' u'nsfnet-igp' u'dgp' u'eigrp' u'tcf' u'sprite-rpc' u'larp' u'mtp'\n",
      " u'ax.25' u'ipip' u'aes-sp3-d' u'micp' u'encap' u'pri-enc' u'gmtp' u'ifmp'\n",
      " u'pnni' u'qnx' u'scps' u'cbt' u'bbn-rcc' u'igp' u'bna' u'swipe' u'visa'\n",
      " u'ipcv' u'cphb' u'iso-tp4' u'wb-expak' u'sep' u'secure-vmtp' u'xtp' u'il'\n",
      " u'rsvp' u'unas' u'fc' u'iso-ip' u'etherip' u'pim' u'aris' u'a/n' u'ipcomp'\n",
      " u'snp' u'compaq-peer' u'ipx-n-ip' u'pgm' u'vrrp' u'l2tp' u'zero' u'ddx'\n",
      " u'iatp' u'stp' u'srp' u'uti' u'sm' u'smp' u'isis' u'ptp' u'fire' u'crtp'\n",
      " u'crudp' u'sccopmce' u'iplt' u'pipe' u'sps' u'ib']\n",
      "service:\n",
      "[u'-' u'http' u'ftp' u'ftp-data' u'smtp' u'pop3' u'dns' u'snmp' u'ssl'\n",
      " u'dhcp' u'irc' u'radius' u'ssh']\n",
      "state:\n",
      "[u'INT' u'FIN' u'REQ' u'ACC' u'CON' u'RST' u'CLO']\n",
      "spkts:\n",
      "[    2     1    10    14    22    20    18    12    16     8     6     4\n",
      "   202    28   246    34    26   224    38    36    48   150    40   116\n",
      "  5416    24  4144   182   252  1642    60   232    62    90    50   136\n",
      "    30    32   118    72    88    52    46    80   160   114   790    54\n",
      "   130    76    78  3684   102   180    44     3  6874   154   284   112\n",
      "   342    74    64   152    82   228   158   822   334   200    42   194\n",
      "   146   144   106   108   262   240   268   372   322    58    68    92\n",
      "   658   320   222    96   196   300   162   366   254   210   378    70\n",
      "   310   346    56   104   798   654   564   192    84  1250  6954  5974\n",
      "    98  1872   184    86   970   298   110  3960   186    66   250   580\n",
      "   294   556   386  1624   148   584   100   248   382   170  9392   694\n",
      "   742   122   132  2494   164  5422 10646  8424   362   128   816   208\n",
      "   548    94   168   226   434   486  1192  3262   424  2726   258   260\n",
      "   256   614   484   140   392   900  6778   416  1118   706   506   626\n",
      "   676   730  5646   332   784   302   316   772  1174  1178   218   120\n",
      "   470   230   640   344   602   234   236   360   328    39   452   338\n",
      "   354   340     5    37   670    21     9    19   142   370    17   400\n",
      "    11   450     7   442   205   686   690   364   305   620   214   314\n",
      "   220   349   317   458    43   660   447   420   336   292   324   356\n",
      "   423   242   353   422   129    27   438   103   352   398   276   426\n",
      "   238   350   448    45   221   351    91    13   333   444   304   358\n",
      "   348    61   212   216    87   206   204   436   432   440   668   384\n",
      "   464   662   524    29    35   225   323   454   326   330   664   502\n",
      "   198   312   456   341   207   402   418   674   319    47   672   650\n",
      "   534   318   646   287   396   638   237    73    41   361   191   105\n",
      "   195   177   313   379   223    51   388   306   197    63   430   201\n",
      "   339   415    59    67   190   395   119   425    81   251   376   357\n",
      "   389    85   327    57   213   410    23   219    15   291   166   355\n",
      "   404    49   209   428   526   121   270  4132   764  1074  1632   188\n",
      "   126   176   820   282   134  1622   572   156   178   264   286    31\n",
      "   138   846   244   544   293   606   496  4812   644   374  5196  1256\n",
      "   172   752   922  1082  5466    25  7340  3638   278  5060  1430   446\n",
      "   550   716   782   272  1874  1940   280   700  5179   562  3760   578\n",
      "  1600  5364   560   124   592  8669  9416  2854 10200  2978   536  2676\n",
      "   797   692  6820   622  5532   898   788   522  2730  1159    83   266]\n",
      "dpkts:\n",
      "[    0     6     8    10    12  1294    20    22    24  1554    28    64\n",
      "    52    14    38    50    26    16   806   622    44     2    54 11018\n",
      "   348   368    18    32    82    46    36    68   116    30    42   236\n",
      "    96   114   306   426     4   656   136   118    58    56    48   812\n",
      "   286    62   156    34    72   634   538   148   566   112    40   208\n",
      "    76   102  1020    66    92   190   448   146   296   220    80  1464\n",
      "   140   162    78   164  1250   320    84   226    60   242   532  1700\n",
      "   152    98   340    86   606   170   230   132   366  1534    94  1428\n",
      "    70   110   338   206   124    74   158   160   362   138   108   252\n",
      "   100   276   256  3692   240   200  1032   376   308   130   896   570\n",
      "   154   300   352   662   150   298   620   228   602    88   210   282\n",
      "   122   694   106   104   204   394   332   334   560   800    90   304\n",
      "   234  1412   126   144   128   378   250   818   608  1582  1272   356\n",
      "   272   120   386  1224   184   258   624  7958   502   274   420   246\n",
      "  3148  1438   810 10872   166   174   402   222  1034  1618   380   290\n",
      "   856   330   484  4518   644  2012   248   358   816   390   430  7728\n",
      "   424   218   142   446   444   418   826   372  3128   134   490   438\n",
      "     3  1278   746  1176   858   119    15   295     1    11    13     7\n",
      "    41   757   163    21   363   684   697   726   652   121   432    27\n",
      "   738   845   630   851   504   740   636   728   125   508   178   436\n",
      "   748   493   819   416    49   423   506   714   735   159   729    39\n",
      "   396   411    33   437   846   742   854  1012     9   713    47   109\n",
      "     5    25    17    67    53   413   648   354   232   768   720   346\n",
      "   577   342   730   238   711   702   421   427   353   434   361   669\n",
      "    43   695   731   244   410   355   743   113   623   723   422   123\n",
      "   428   727   766   347   718    61   326   788    29    59    71   431\n",
      "   673   689   221   328    75   824   646   701   724    35   675    55\n",
      "   224   780   739   409   633   360   672   693   383   400   309   216\n",
      "   829   692   682  1722   530   292 10970   464   278   370   314   374\n",
      "   668   280   462  3784   480   696   188   628    19   254   906  1212\n",
      "   212   324   310  3504    23   750   388  1349    37   182   784  2668\n",
      "   466   266   405   382   318    69   408  7090   828  1100   548   168\n",
      "  1812   582   770  2929   796   302  2030    83  1724   805  1386   586\n",
      "   172    31   549   802   180   197   186  1230   559  1446   201  1340\n",
      "  1418   478  1038   486  1518   414  1984   890  3360   452   600   322\n",
      "   654  1028  1014   336   404    57   863  5902  4455  1324   288   804\n",
      "   704  2194  1163  1432]\n",
      "sbytes:\n",
      "[   496   1762   1068 ...,   3376 135414 138067]\n",
      "dbytes:\n",
      "[    0   268   354 ...,  9148  9232 15365]\n",
      "rate:\n",
      "[  9.09090902e+04   1.25000000e+05   2.00000005e+05 ...,   1.84748620e+01\n",
      "   2.53511920e+01   2.44100670e+01]\n",
      "sttl:\n",
      "[254   0  62 255  31  60   1  32  29  30  63]\n",
      "dttl:\n",
      "[  0 252  60  29  31  30  32 253]\n",
      "sload:\n",
      "[  1.80363632e+08   8.81000000e+08   8.54400000e+08 ...,   5.22222803e+03\n",
      "   2.11771859e+05   1.24104398e+05]\n",
      "dload:\n",
      "[    0.        1249.506714  1288.789062 ...,  2207.130127  1546.616211\n",
      "  2242.109863]\n",
      "sloss:\n",
      "[   0    2    4   21   19   17   15   13   11    9    7    5    3    1   31\n",
      "  109   16   72    6   14 2705    8 2070   88  123  113   10   42   22   65\n",
      "   12   50   33   41   23   20   37   77  391   24   35   36   56 1841   87\n",
      " 3436   27   74  139   53  168   28   29   98  120   73   38   54   76  409\n",
      "  164   18   94   70   51  117   34  131  183   26   43  326  157  108   95\n",
      "  146   78  180   55  124  102  186   32  152  170   49  396  324  279   93\n",
      "   39  622 3476 2986   48  933   40   68  478 1976   90   30   69  286  144\n",
      "  275  189  809   71  289   47   25  188   82 4694  343  368   58   63 1241\n",
      "   79 2707 5319 4210  178   61  407  274   44   81  110  216  240 1627  209\n",
      " 1360  126  127  125  304  193  449 3386  205  556  350  250  310  337  362\n",
      " 2820  161  389   75  154  386  586  145  106   59   66   52   45 2065  381\n",
      "  535   91   85  403   57  138   46   64   89   86   92  128   80   83  140\n",
      "   60   67  420  111  150  165  345  119  269  281 2405  219  319  184  167\n",
      "  118 2594  625  422  259  190   62  375  460 2730  121 3669 1812  156  202\n",
      " 2528  712  171  272  355  388  132  934  967  347 2535  278 1876  797  179\n",
      " 2679  130  276  293 4206 4707 1424  122 5096 1484 1335  394 3407  308  257\n",
      " 1362  159  546  210  104  103  100   99   97  129  107  101   96]\n",
      "dloss:\n",
      "[   0    1    2    3  645    7    9    4    8   10  775   11   30   23   17\n",
      "    6    5   12   20 5507  172  182   38   39   21   32   56  116   44   54\n",
      "  151  211   13  326   16   57   27   25   22  403  141   19   29   76   34\n",
      "  267   72  281   18   14   24  102   36   49   55   93  222   71  146  108\n",
      "   37  729   79   31   80  623   15  158   40  111   28  118  264   26  848\n",
      "  181   74   47  168   68   41  301   82  113   64  765   45  712   33   53\n",
      "  167  101   60   35   77   78  179   83   52  124  135  126 1844  185   63\n",
      "  283  136   75  174  329   46  308  112   48   42  103  139   59  345   50\n",
      "  100  195  164  165  278   43  150  114   61   67   70  123  302  176  134\n",
      "   58  610   89  127  310 3978  121  184 1572  717 5436   81   62   85  109\n",
      "  806  163  240   66 2257  320 1004  122  177  406 3864  207  210  107   69\n",
      "  221  220  410  183 1558   65  243  197  583  370  536  390  104  155  323\n",
      "  190  337  353   51  193  362  381  286  386  364  274  288  356  340  196\n",
      "  227  365  187  360  349  357  178  358  383  367  461  295  361  354  368\n",
      "  273   98  369  352  318  191  156  327  336  128  142  189  298  319  332\n",
      "  346  145  194  355  137  334  300  317   99   97  304  169  175  359  348\n",
      "  312  206  411  859  263  144 5483  230  115  138  180 1886  237   92  106\n",
      "  247  125  451  604  148 1561  152  153 1750  215  192  608 1331  120  157\n",
      "  202 3545  904  289 1436  299 1013  860  691  188  143  242  119   87   96\n",
      "   90  613  262  721  517  214  186  205  990  442 1676  217  154  325  504\n",
      "  166  199 2950 2193  641  241  400  350 1091  535  714]\n",
      "sinpkt:\n",
      "[  1.10000000e-02   8.00000000e-03   5.00000000e-03 ...,   8.32163330e+01\n",
      "   4.82877670e+01   5.58800510e+01]\n",
      "dinpkt:\n",
      "[   0.        270.886813  261.252    ...,  138.888594  221.155219  143.7     ]\n",
      "sjit:\n",
      "[     0.        10042.8699    13125.11139  ...,   4368.102356   9709.118613\n",
      "   4798.130981]\n",
      "djit:\n",
      "[   0.        468.508156  399.405156 ...,  195.623781  559.5605    190.980813]\n",
      "swin:\n",
      "[  0 255 156 202  99   5  67 154  52 245  43]\n",
      "stcpb:\n",
      "[         0 1640845588 2214319888 ..., 3487870721 2309382861 1072535109]\n",
      "dtcpb:\n",
      "[         0  177111456 3374287000 ..., 2884385558 4150989117 3284291478]\n",
      "dwin:\n",
      "[  0 255  81 160  77 171 137 164  27  33 209  12  46 125]\n",
      "tcprtt:\n",
      "[ 0.        0.148966  0.185096 ...,  0.237125  0.140691  0.173208]\n",
      "synack:\n",
      "[ 0.        0.079731  0.095516 ...,  0.083616  0.117467  0.100191]\n",
      "ackdat:\n",
      "[ 0.        0.069235  0.08958  ...,  0.119658  0.05986   0.073017]\n",
      "smean:\n",
      "[ 248  881  534 ...,  515 1015  523]\n",
      "dmean:\n",
      "[   0   45   44 ...,  610 1377 1033]\n",
      "trans_depth:\n",
      "[  0   1 131   2   9   3   4   8]\n",
      "response_body_len:\n",
      "[   0  184  187 ...,  192  147 1492]\n",
      "ct_srv_src:\n",
      "[ 2  3  1  4  5 17 18  8 11 12  6 27 10  7  9 37 39 63 28 43 14 20 13 36 22\n",
      " 29 19 15 24 59 30 35 41 45 21 47 38 48 23 25 32 44 26 33 16 34 42 49 51 31\n",
      " 50 40 52 46 54 53 58]\n",
      "ct_state_ttl:\n",
      "[2 1 6 4 0 3 5]\n",
      "ct_dst_ltm:\n",
      "[ 1  2 18  4  9  3  5  8 19 20 11  6  7 14 15 10 12 35 13 24 16 42 30 17 26\n",
      " 41 27 25 47 37 32 31 43 34 33 39 36 29 28 21 45 38 22 44 23 40 59 52 50 48]\n",
      "ct_src_dport_ltm:\n",
      "[ 1  2  3  4 10 14 15  9  6  8  5 12  7 13 11 18 16 42 30 17 26 41 27 25 47\n",
      " 24 19 37 32 31 43 34 33 39 36 35 20 29 28 21 45 38 22 44 23 40 59 52 50 48]\n",
      "ct_dst_sport_ltm:\n",
      "[ 1  2  3  4 10  6  5  8 18 14  9 26 11 16 25 12 37  7 13 27 24 21 19 23 22\n",
      " 15 17 20 28 33 32 31 38]\n",
      "ct_dst_src_ltm:\n",
      "[ 2  3  1 14 17  7 11  9  4 18 25 10 63 12  6  5 37 39  8 28 40 20 13 36 22\n",
      " 29 15 21 23 34 24 26 59 30 35 41 45 47 38 48 32 43 44 19 16 42 49 51 31 27\n",
      " 50 33 52 46 54 53 58]\n",
      "is_ftp_login:\n",
      "[0 1 2]\n",
      "ct_ftp_cmd:\n",
      "[0 1 2]\n",
      "ct_flw_http_mthd:\n",
      "[ 0  1  9  4  2 16 12  6]\n",
      "ct_src_ltm:\n",
      "[ 1  2  3 10  4  5  9 11 22  6  7 14 15 13 12 19 21 23  8 18 35 17 16 27 20\n",
      " 43 30 42 26 25 47 24 37 32 31 34 33 39 36 29 45 38 44 40 60 52 50 48 41 28]\n",
      "ct_srv_dst:\n",
      "[ 2  3  1 15 17  8 11  9  4  5 25 10  7 12  6 37 39 62 28 20 13 36 30 22 29\n",
      " 14 27 23 18 59 35 41 45 21 47 38 48 32 43 44 26 19 33 16 34 24 42 49 51 31\n",
      " 50 40 52 46 54 53 58]\n",
      "is_sm_ips_ports:\n",
      "[0 1]\n",
      "attack_cat:\n",
      "[u'Normal' u'Reconnaissance' u'Backdoor' u'DoS' u'Exploits' u'Analysis'\n",
      " u'Fuzzers' u'Worms' u'Shellcode' u'Generic']\n",
      "label:\n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "# List the unique values of each column\n",
    "header = df.columns.values.tolist()\n",
    "for column in header:\n",
    "    print column + \":\\n\", df[column].unique()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This section will handle any adjustments to the dataset to handle issues like missing, incorrect values etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Give simple, appropriate statistics (range, mode, mean, median, variance, counts, etc.) for the most important attributes and describe what they mean or if you found something interesting. \n",
    "    - Note: You can also use data from other sources for comparison. Explain the significance of the statistics run and why they are meaningful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the most important attributes appropriately (at least 5 attributes).\n",
    "    - Important: Provide an interpretation for each chart. Explain for each attribute why the chosen visualization is appropriate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore relationships between attributes: Look at the attributes via scatter plots, correlation, cross-tabulation, group-wise averages, etc. as appropriate. \n",
    "    - Explain any interesting relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Based on outlier identification of the features spkts and dpkts the following analysis is conducted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent of packets that have dpkts > 10 and abnormal = 30.4111342185\n",
      "percent of packets that have dpkts > 10 and normal = 69.5888657815\n"
     ]
    }
   ],
   "source": [
    "\n",
    "denom = df[(df['dpkts'] > 10)]\n",
    "numerator = df[(df['dpkts'] > 10) & (df['label'] == 1)]\n",
    "print \"percent of packets that have dpkts > 10 and abnormal = \" + str((float(len(numerator))/len(denom)) * 100)\n",
    "numerator  = df[(df['dpkts'] > 10) & (df['label'] == 0)]\n",
    "print \"percent of packets that have dpkts > 10 and normal = \" + str((float(len(numerator))/len(denom)) * 100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All things being equal,  I would expect a ratio of less than or greater than 50% between normal to abnormal packets to be something interesting that maybe useful in detection.  there is about 40% less destination to source packets for abnormal packets.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent of packets that have spkts > 12 and abnormal = 34.5773825564\n",
      "percent of packets that have spkts > 12 and normal = 65.4226174436\n"
     ]
    }
   ],
   "source": [
    "#col_dpkts = df['dpkts', 'label']\n",
    "denom = df[(df['spkts'] > 10)]\n",
    "numerator = df[(df['spkts'] > 10) & (df['label'] == 1)]\n",
    "print \"percent of packets that have spkts > 12 and abnormal = \" + str((float(len(numerator))/len(denom)) * 100)\n",
    "numerator  = df[(df['spkts'] > 10) & (df['label'] == 0)]\n",
    "print \"percent of packets that have spkts > 12 and normal = \" + str((float(len(numerator))/len(denom)) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All things being equal, I would expect a ratio of less than or greater than 50% between normal to abnormal packets to be something interesting that maybe useful in detection, there is about 30% less source to destination to source packets for abnormal packets. Flagged for further analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label_cat              abnormal  normal\n",
      "dpkts_cat                              \n",
      "expected_dpkts_size       39629   23950\n",
      "unexpected_dpkts_size      5703   13050\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x287c4198>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x229d7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#defensive copy on original dataframe.\n",
    "df2 = df.copy(deep=True)\n",
    "bins =[-1, 10, 11019] # Note: values needed to be slighlty greater than the min and max values to ensure the get counted.\n",
    "bins2 = [-1, .5, 2]  # created bins 2 to rename 0, 1 ( false, true) to normal, abnormal to intepret plot easier.\n",
    "group_names = ['expected_dpkts_size', 'unexpected_dpkts_size']\n",
    "group_names2 = ['normal', 'abnormal']\n",
    "categories = pd.cut(df2['dpkts'], bins, labels=group_names)\n",
    "categories2 = pd.cut(df2['label'], bins2, labels=group_names2)\n",
    "df2['dpkts_cat'] =  pd.cut(df2['dpkts'], bins, labels=group_names)\n",
    "df2['label_cat'] =  pd.cut(df2['label'], bins2, labels=group_names2)\n",
    "#abnormal_counts = pd.crosstab([df2['dpkts_cat']], df2.label.astype(bool))\n",
    "abnormal_counts = pd.crosstab([df2['dpkts_cat']], df2.label_cat.astype(str))\n",
    "print abnormal_counts\n",
    "#abnormal_counts.plot(kind='bar', stacked=True, color=['green','red'], figsize=(20,4))\n",
    "abnormal_counts.plot(kind='bar', stacked=True, color=['red','green'], figsize=(20,4)) # swap color when switch out label to abnormal/normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to note that for the expected_dpkts_size (which is defined as anything less than the 75% quartile (< 10)\n",
    "there are more abnormal packets than normal ones. Conversely, for unexpected dpkts size (>=10) the reverse is true. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify and explain interesting relationships between features and the class you are trying to predict (i.e., relationships with variables and the target classification)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note: In the analysis in the \"Explore relationships between attributes\" section the cross tab of dpkts and label (normal/abnormal) fulfills this requirment as we believe there may be a relationship between dpkts (indpependent) and label( depenent variable [variable trying to predict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'label', u'spkts', u'dpkts', u'dbytes', u'sbytes', u'rate', u'sloss', u'dloss', u'state', u'ct_dst_src_ltm', u'ct_srv_src', u'ct_src_ltm']\n"
     ]
    },
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8PhITE/n000+j/GomBrXKz+Hy3ahV/miHMqFJnntIHsSlqqis5o3NO6iorKYw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hs8+uijo9Ec0YclCy9InpYzUU49rZySz/6aJpYsvGDYDxhdB7Am/mn+Qjk40bvehBhqUl/iZENZ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OdZalasB8kFdV7EQh7EZJRkIkYqmcDn6yGVSiEmYkQDbpKxEPF4FFNBORPOHhQdB5hw9mTK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\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Hrc3Zs2eZO3cuq1at4tNPPyUzM5MvvviCHj16EBQUhFqtxtramuXLl1O/fn0T9k4ikVQ2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\nAAhJREFUpAj/H4vz2ZWokbQnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "df_sub = df[['label', 'spkts', 'dpkts', 'dbytes', 'sbytes', 'rate', 'sloss', 'dloss', 'state', 'ct_dst_src_ltm', 'ct_srv_src', 'ct_src_ltm' ]]\n",
    "df_corr = df_sub.corr(method='pearson')\n",
    "\n",
    "header = df_sub.columns.values.tolist()\n",
    "print header\n",
    "len(header)\n",
    "scatter_matrix(df_sub,figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the scatter plot matrix, there appears to be varying degrees of  positive linear correlation between the following features:\n",
    "dbytes - dloss; dpkts - dloss; sbytes  sloss; spkts - sloss; ct_srv_src - ct_dst_src_ltm; dpkts - dbytes\n",
    "Other features are more difficult to find signifiant linear correlation due to binary nature of variable. \n",
    "heatmap below will help identify them visually based on color gradient which relates to strength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sloss</th>\n",
       "      <th>dloss</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027731</td>\n",
       "      <td>-0.061515</td>\n",
       "      <td>-0.032632</td>\n",
       "      <td>0.020641</td>\n",
       "      <td>0.328629</td>\n",
       "      <td>0.006360</td>\n",
       "      <td>-0.044399</td>\n",
       "      <td>0.279989</td>\n",
       "      <td>0.290195</td>\n",
       "      <td>0.276494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spkts</th>\n",
       "      <td>-0.027731</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.369554</td>\n",
       "      <td>0.198324</td>\n",
       "      <td>0.965750</td>\n",
       "      <td>-0.068249</td>\n",
       "      <td>0.973644</td>\n",
       "      <td>0.198683</td>\n",
       "      <td>-0.061852</td>\n",
       "      <td>-0.058717</td>\n",
       "      <td>-0.049367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dpkts</th>\n",
       "      <td>-0.061515</td>\n",
       "      <td>0.369554</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976419</td>\n",
       "      <td>0.175834</td>\n",
       "      <td>-0.083173</td>\n",
       "      <td>0.189060</td>\n",
       "      <td>0.981506</td>\n",
       "      <td>-0.075012</td>\n",
       "      <td>-0.062836</td>\n",
       "      <td>-0.057374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dbytes</th>\n",
       "      <td>-0.032632</td>\n",
       "      <td>0.198324</td>\n",
       "      <td>0.976419</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>-0.047978</td>\n",
       "      <td>0.014561</td>\n",
       "      <td>0.997109</td>\n",
       "      <td>-0.044048</td>\n",
       "      <td>-0.034330</td>\n",
       "      <td>-0.033301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sbytes</th>\n",
       "      <td>0.020641</td>\n",
       "      <td>0.965750</td>\n",
       "      <td>0.175834</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025102</td>\n",
       "      <td>0.995027</td>\n",
       "      <td>0.007091</td>\n",
       "      <td>-0.024065</td>\n",
       "      <td>-0.030204</td>\n",
       "      <td>-0.021540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate</th>\n",
       "      <td>0.328629</td>\n",
       "      <td>-0.068249</td>\n",
       "      <td>-0.083173</td>\n",
       "      <td>-0.047978</td>\n",
       "      <td>-0.025102</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040139</td>\n",
       "      <td>-0.062073</td>\n",
       "      <td>0.358902</td>\n",
       "      <td>0.367670</td>\n",
       "      <td>0.327563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sloss</th>\n",
       "      <td>0.006360</td>\n",
       "      <td>0.973644</td>\n",
       "      <td>0.189060</td>\n",
       "      <td>0.014561</td>\n",
       "      <td>0.995027</td>\n",
       "      <td>-0.040139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014661</td>\n",
       "      <td>-0.035797</td>\n",
       "      <td>-0.040117</td>\n",
       "      <td>-0.031760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dloss</th>\n",
       "      <td>-0.044399</td>\n",
       "      <td>0.198683</td>\n",
       "      <td>0.981506</td>\n",
       "      <td>0.997109</td>\n",
       "      <td>0.007091</td>\n",
       "      <td>-0.062073</td>\n",
       "      <td>0.014661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056492</td>\n",
       "      <td>-0.045932</td>\n",
       "      <td>-0.043066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <td>0.279989</td>\n",
       "      <td>-0.061852</td>\n",
       "      <td>-0.075012</td>\n",
       "      <td>-0.044048</td>\n",
       "      <td>-0.024065</td>\n",
       "      <td>0.358902</td>\n",
       "      <td>-0.035797</td>\n",
       "      <td>-0.056492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.933795</td>\n",
       "      <td>0.840012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ct_srv_src</th>\n",
       "      <td>0.290195</td>\n",
       "      <td>-0.058717</td>\n",
       "      <td>-0.062836</td>\n",
       "      <td>-0.034330</td>\n",
       "      <td>-0.030204</td>\n",
       "      <td>0.367670</td>\n",
       "      <td>-0.040117</td>\n",
       "      <td>-0.045932</td>\n",
       "      <td>0.933795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.822486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <td>0.276494</td>\n",
       "      <td>-0.049367</td>\n",
       "      <td>-0.057374</td>\n",
       "      <td>-0.033301</td>\n",
       "      <td>-0.021540</td>\n",
       "      <td>0.327563</td>\n",
       "      <td>-0.031760</td>\n",
       "      <td>-0.043066</td>\n",
       "      <td>0.840012</td>\n",
       "      <td>0.822486</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   label     spkts     dpkts    dbytes    sbytes      rate  \\\n",
       "label           1.000000 -0.027731 -0.061515 -0.032632  0.020641  0.328629   \n",
       "spkts          -0.027731  1.000000  0.369554  0.198324  0.965750 -0.068249   \n",
       "dpkts          -0.061515  0.369554  1.000000  0.976419  0.175834 -0.083173   \n",
       "dbytes         -0.032632  0.198324  0.976419  1.000000  0.010036 -0.047978   \n",
       "sbytes          0.020641  0.965750  0.175834  0.010036  1.000000 -0.025102   \n",
       "rate            0.328629 -0.068249 -0.083173 -0.047978 -0.025102  1.000000   \n",
       "sloss           0.006360  0.973644  0.189060  0.014561  0.995027 -0.040139   \n",
       "dloss          -0.044399  0.198683  0.981506  0.997109  0.007091 -0.062073   \n",
       "ct_dst_src_ltm  0.279989 -0.061852 -0.075012 -0.044048 -0.024065  0.358902   \n",
       "ct_srv_src      0.290195 -0.058717 -0.062836 -0.034330 -0.030204  0.367670   \n",
       "ct_src_ltm      0.276494 -0.049367 -0.057374 -0.033301 -0.021540  0.327563   \n",
       "\n",
       "                   sloss     dloss  ct_dst_src_ltm  ct_srv_src  ct_src_ltm  \n",
       "label           0.006360 -0.044399        0.279989    0.290195    0.276494  \n",
       "spkts           0.973644  0.198683       -0.061852   -0.058717   -0.049367  \n",
       "dpkts           0.189060  0.981506       -0.075012   -0.062836   -0.057374  \n",
       "dbytes          0.014561  0.997109       -0.044048   -0.034330   -0.033301  \n",
       "sbytes          0.995027  0.007091       -0.024065   -0.030204   -0.021540  \n",
       "rate           -0.040139 -0.062073        0.358902    0.367670    0.327563  \n",
       "sloss           1.000000  0.014661       -0.035797   -0.040117   -0.031760  \n",
       "dloss           0.014661  1.000000       -0.056492   -0.045932   -0.043066  \n",
       "ct_dst_src_ltm -0.035797 -0.056492        1.000000    0.933795    0.840012  \n",
       "ct_srv_src     -0.040117 -0.045932        0.933795    1.000000    0.822486  \n",
       "ct_src_ltm     -0.031760 -0.043066        0.840012    0.822486    1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XryY3N5fmzZtz6dIlzp8/T/fu3cnMzGTOnDnlkvNuWmo9uGw22WRdt6PLz8fT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\nGREREZFgLMiIiIiIBGNBRkRERCTY/wESDGj+s3YdwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228b0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(220, 10, as_cmap=True) # one of the many color mappings\n",
    "sns.set(style=\"darkgrid\") # one of the many styles to plot using\n",
    "\n",
    "f, ax = plt.subplots(figsize=(9, 9))\n",
    "\n",
    "sns.heatmap(df_corr, cmap=cmap, annot=True)\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Are there other features that could be added to the data or created from existing features? Which ones? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note: In the analysis in the \"Explore relationships between attributes\" section the cross tab of dpkts and label (normal/abnormal) fulfills this requirment as we created 'dpkts_cat' and 'label_cat' from the existing variables in continous to categorial conversation to be able to do cross tab analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca: [[  2.15334058e-06   1.33232425e-10   7.14057931e-09   7.89360016e-09\n",
      "    3.32817382e-06   5.97994699e-06  -4.00072404e-05  -1.94762218e-08\n",
      "    4.46247972e-08  -3.16511105e-02   2.69260142e-04   2.09605683e-09\n",
      "    2.90366574e-09  -2.97219541e-07   4.19874834e-08   3.05125441e-06\n",
      "    2.58094416e-07   5.93836089e-08   7.10360849e-01   7.03125591e-01\n",
      "    6.18332912e-08   2.74782038e-11   1.44130851e-11   1.30651187e-11\n",
      "    1.54294435e-08   5.03906261e-08   4.55120329e-11   7.17975798e-07\n",
      "   -2.56476092e-09  -2.74950112e-10  -1.62759342e-09  -1.69363511e-09\n",
      "   -1.26869727e-09  -2.49672648e-09   3.88501775e-12   3.92762680e-12\n",
      "    6.40132775e-11  -1.74892840e-09  -2.62205348e-09  -5.40418795e-12\n",
      "   -8.81062313e-11]\n",
      " [  5.59383610e-08   6.37312512e-12   5.25093746e-10  -4.03528655e-10\n",
      "    1.04788177e-06  -6.05700054e-07  -1.52249573e-08  -4.28572404e-11\n",
      "    1.98073413e-10  -6.86273602e-06   2.05265291e-05   3.41472684e-10\n",
      "   -2.11615443e-10  -5.48761353e-09  -9.98394525e-09  -5.02468871e-07\n",
      "   -3.88444785e-08  -1.43781970e-11  -7.03478233e-01   7.10716804e-01\n",
      "    8.75817947e-11   6.25893085e-13   5.74502784e-13   5.13903006e-14\n",
      "    8.39891708e-10  -1.05193310e-09   3.09498766e-12  -3.17891594e-09\n",
      "    1.86946136e-12   3.05054719e-12   6.63378994e-12  -5.68095718e-12\n",
      "   -4.18384811e-12   1.35250870e-12  -9.45986625e-13  -1.09703456e-12\n",
      "    7.03569961e-12   3.78273281e-12  -3.99267943e-12  -7.45060620e-14\n",
      "   -1.70035201e-12]]\n",
      "lda: [[  1.89201438e-05   1.22142484e-02   5.80878465e-03   2.11804515e-03\n",
      "    4.79356897e-07   1.03165778e-05  -1.37033876e-06  -5.35909259e-03\n",
      "   -2.35886337e-03   1.68660323e-09   6.55217669e-08  -1.43084391e-02\n",
      "   -3.39688754e-02   1.92968511e-05  -4.41473961e-06   5.72972907e-07\n",
      "   -1.01222813e-05   1.86442745e-02  -5.39114120e-12  -9.53890081e-13\n",
      "   -1.48061575e-02   7.47509322e-01   1.42213269e+00   9.17599414e-01\n",
      "    1.75704760e-06  -1.38963011e-03  -6.38776397e-02  -5.94284981e-07\n",
      "   -2.10833255e-02  -7.24997987e-01   1.44572904e-02  -5.98686346e-02\n",
      "   -1.18124992e-01   9.00375979e-02  -1.53234695e-01  -3.27144436e-01\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\lda.py:371: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "#ref: http://localhost:8888/notebooks/DataMiningNotebooks/03.%20Dimension%20Reduction.ipynb\n",
    "\n",
    "# Lets apply PCA against our numeric variables to see if we can find new component features that my reduce\n",
    "# the dimensionality of our dataset, but still explain it well.\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.lda import LDA\n",
    "df_copy = df.select_dtypes(['float64', 'int64'])\n",
    "\n",
    "y = df.attack_cat\n",
    "#df_copy.drop('attack_cat', axis=1, inplace=True) \n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_PCA = pca.fit(df_copy).transform(df_copy)\n",
    "\n",
    "lda = LDA(n_components=2)\n",
    "X_lda = lda.fit(df_copy, y).transform(df_copy) # fit data and then transform it\n",
    "\n",
    "#print the components\n",
    "print'pca:', pca.components_\n",
    "print 'lda:', lda.scalings_.T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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